Yong Zhao
ABSTRACT
In many computer vision tasks, the available original data is in matrix form. Traditional methods often convert a matrix into a vector before processing them. This kind of methods not only ignore the location information of matrix elements, but have to deal with the high-dimensional vectors. Two-dimensional linear discriminant analysis (2DLDA) is a widely used approach in image recognition which works with data matrix directly and computes efficiently. When mapping the original data onto low-dimensional space, however, the two projection matrices of 2DLDA cannot remove features with little or no information, resulting in redundant features in the projected space. To address the problem, in this paper we propose an algorithm named two-dimensional discriminative feature selection (2DDFS) for bidirectional direct feature selection on matrix data directly. Based on 2DLDA, it employs the l2,1 norm to regularize the two transformation matrices when learning them. To obtain the optimal solutions, we design an effective optimization algorithm. Then we conduct experiments on two image databases to evaluate the performance of the proposed method, by comparing with other related methods. The promising results demonstrate the effectiveness of our method.
- Turk, M., & Pentland, A. (1991). Eigenfaces for recognition. Journal of Cognitive Neuroscience, 3(1), 71--86. Google Scholar
Digital Library
- He, X., & Niyogi, P. (2003). Locality Preserving Projections. in Advances in Neural Information Processing Systems. Cambridge, MA: MIT Press, 2003. Google ScholarDigital Library
- Nie, F., Xiang, S., Song, Y., & Zhang, C. (2009). Orthogonal locality minimizing globality maximizing projections for feature extraction. Optical Engineering, 48(1), 017202-1-017202-5.Google Scholar
- Tao, D., Li, X., Hu, W., Maybank, S., & Wu, X. (2005). Supervised tensor learning. In Fifth IEEE International Conference on Data Mining (ICDM'05). Google ScholarDigital Library
- Yang, J., Zhang, D. D., Frangi, A. F., & Yang, J. Y. (2004). Two-dimensional PCA: a new approach to appearance-based face representation and recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(1), 131--137. Google ScholarDigital Library
- Ye, J., Janardan, R., & Li, Q. (2005). Two-dimensional linear discriminant analysis. In Advances in Neural Information Processing Systems, 1569--1576. Google ScholarDigital Library
- Gabriel, K. R. (1998). Generalised bilinear regression. Biometrika, 85(3), 689--700.Google ScholarCross Ref
- Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of Machine Learning Research, 3(Mar, 2003), 1157--1182. Google ScholarDigital Library
- Wang, L., Zhou, N., & Chu, F. (2008). A general wrapper approach to selection of class-dependent features. IEEE Transactions on Neural Networks, 19(7), 1267--1278. Google ScholarDigital Library
- Liu, X., Wang, L., Zhang, J., Yin, J., & Liu, H. (2014). Global and local structure preservation for feature selection. IEEE Transactions on Neural Networks and Learning Systems, 25(6), 1083--1095.Google ScholarCross Ref
- Chang, X., Nie, F., Yang, Y., Zhang, C., & Huang, H. (2016). Convex sparse PCA for unsupervised feature learning. ACM Transactions on Knowledge Discovery from Data (TKDD), 11(1). Google ScholarDigital Library
- Tao, H., Hou, C., Nie, F., Jiao, Y., & Yi, D. (2016). Effective discriminative feature selection with nontrivial solution. IEEE Transactions on Neural Networks and Learning Systems, 27(4), 796--808.Google ScholarCross Ref
- Hou, C., Nie, F., Yi, D., & Wu, Y. (2013). Efficient image classification via multiple rank regression. IEEE Transactions on Image Processing, 22(1), 340--352. Google ScholarDigital Library
- Hou, C., Jiao, Y., Nie, F., Luo, T., & Zhou, Z. H. (2017). 2D feature selection by sparse matrix regression. IEEE Transactions on Image Processing, 26(9), 4255--4268.Google ScholarDigital Library
- CASIA.v4 Iris Database: http://biometrics.idealtest.org/dbDetailForUser.do?id=4, 2010Google Scholar
- OpenCV based face and eye detector: https://www.codeproject.com/articles/23191/face-and-eyes-detection-using-opencv, 2008Google Scholar
- Zhao, Z., & Ajay, K. (2015). An accurate iris segmentation framework under relaxed imaging constraints using total variation model. In Proceedings of the IEEE International Conference on Computer Vision, 3828--3836. Google ScholarDigital Library
Index Terms
- Two-Dimensional Discriminative Feature Selection for Image Recognition
Recommendations
Locality preserving multimodal discriminative learning for supervised feature selection
Feature selection has been an important preprocessing step in high-dimensional data analysis and pattern recognition. In this paper, we propose a locality preserving multimodal discriminative learning method called LPMDL for supervised feature selection,...
Sparse discriminative feature selection
As sparse representation-based classifier (SRC) is developed, it has drawn more and more attentions in dimension reduction. In this paper, we introduce SRC based measurement criterion into feature selection, and then propose a novel method called sparse ...
Separable linear discriminant analysis
Linear discriminant analysis (LDA) is a popular technique for supervised dimension reduction. Due to the curse of dimensionality usually suffered by LDA when applied to 2D data, several two-dimensional LDA (2DLDA) methods have been proposed in recent ...
Comments